Nettsider med emneord «Monte Carlo methods»
The objective of StREP is to provide a comprehensive and unified account of the decision to request public votes and the consequences thereof for observed behaviour.
The project consists in investigating the non-linear structure formation within the framework of dark energy, dark matter and modified gravity theories using N-body and hydrodynamic numerical simulations.
The spatial configuration of continents and its temporal evolution exert a fundamental control on Earth’s evolution. Before 130 Ma, plate motions can only be quantified through the study of paleomagnetism, however, individual paleomagnetic data cannot constrain longitude.
LATICE aims to advance the knowledge base concerning land atmosphere interactions through improved model representation of snow, permafrost, hydrology and large-scale vegetation processes representative of high latitudes, including; 1) New ground observations (gap filling using ML); 2) Land surface model parametrization using data science methods; 3) Seasonal snow cover dynamics using data assimilation and Earth observations (e.g. satellite data, drones).
We use quantum mechanical theory (density functional theory) and develop statistical methods such as Monte Carlo techniques, molecular dynamics, thermodynamic integration, genetic algorithms in conjunction with machine learning to understand more about deep earth processes and core-mantle
interactions.
The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice/water balance for permafrost and glaciers. The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming.
Exploring the fundamental constituents of the Universe physicists are faced with very serious calculational bottlenecks. To compare new physics models to data we need to perform very computationally expensive calculations in quantum field theory (QFT).
For maritime safety surveillance we develop new approaches
based on the availability of large arrays of sensors, which
monitor condition and performance of vessels, machinery, or
power systems.
BigInsight produces innovative solutions for key data-driven challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.
The Centre for Earth Evolution and Dynamics (CEED) is a Centre of Excellence dedicated to research of fundamental importance to the understanding of our planet.
Modern science usually provides both copious amounts of data and complicated models for the part of reality it is trying to describe. Often there is even so much data, and the models so complicated, that it becomes difficult to make full use of the data in deciding which models best describe the world around us, and finding their properties. The main goal of the GAMBIT project is to develop a software tool to help physicists do just that.
Bayesian methods have recently regained a significant amount of attention in the machine community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated.